Rust – Faster compilation with the parallel front-end in nightly

Original Article ↗ Hacker News Discussion ↗

Rust’s compiler is gaining a new parallel front-end mode in nightly builds, aiming to speed up compilation of large crates that previously could not exploit multi-core CPUs effectively. Programmers report widely varying real-world compile times—from near-instant builds on fast laptops to 20–30 minute clean builds in large monorepos—highlighting how dependency counts, code generation, linkers, and hardware all shape performance, and why further improvements (e.g., binary dependencies, alternative backends, faster linkers) still matter. Alongside technical details, many comments reflect Rust’s unusual popularity: users praise its tooling, safety guarantees, and “developer experience,” while others question the intensity of the hype and compare it to earlier language waves like Ruby, Go, and Java.

Current and new parallelism in rustc

  • Existing parallelism is mostly at the crate level (per rustc process), not per file/module in the front-end; LLVM back-end already parallelizes via codegen units.
  • Large monolithic crates therefore underutilize many-core machines; splitting into more crates has costs.
  • The new nightly parallel front-end introduces intra-crate parallelism and coordinates with Cargo via jobserver, so it’s not expected to “steal” parallelism from crate-level builds.
  • Feature is experimental: uses -Z threads, can deadlock or hang; defaults to 1 thread.

Observed compile times and influencing factors

  • Experiences vary widely: some report near-instant builds on fast laptops for small/mid-sized projects; others see many minutes for large monorepos (tens to hundreds of kLoC and hundreds of dependencies).
  • Dependencies, proc-macros, codegen (e.g., protobuf), and native deps (e.g., zstd, librdkafka, OpenSSL) are cited as heavy drivers of time.
  • Linker choice matters: mold can significantly speed linking vs default linkers.
  • Network filesystems can dramatically slow builds compared to local disks.
  • Some monorepos report 10–30 minute fully optimized CI builds consuming large RAM (e.g., 50GB with LTO).

Tooling, optimization ideas, and limits

  • rust-analyzer often uses more CPU/RAM than rustc, but many consider it worth the cost.
  • Suggestions for future speedups: precompiled/binary dependencies (especially build scripts and proc-macros), Cranelift back-end for fast dev builds, better linkers, wasm-sandboxed macros/build scripts.
  • Some argue big remaining wins are limited; compiler is already heavily optimized and much time is in LLVM/codegen, not borrow checking.
  • Others think 2–3× overall improvements are still realistic (e.g., from binary artifacts and alternative backends), but Rust likely will never match Go-level compile speed due to language complexity and monomorphization.

CI, caching, and configuration

  • Docker-based builds and ephemeral CI runners complicate caching and can inflate times; strategies vary from aggressive cache reuse to periodic cache clears.
  • RUSTFLAGS="-Z threads=N" or environment detection (nproc) are used now; future stable behavior is expected to align with core count and jobserver.

Rust popularity and “marketing”

  • Several comments debate why Rust gets so much hype: pent-up demand for safe systems programming, strong tooling (Cargo/crates.io), and good developer experience versus C/C++.
  • Some view enthusiasm as genuine grassroots; others see it as a repeating hype cycle similar to past waves (Ruby, Java, Go).